 What's going on everybody? Welcome back to another video. Today we're looking at the difference between a data analyst and a data scientist. Now I myself have been a data analyst for many years but I've worked with a lot of data scientists and what we're going to do is break down category by category the difference between a data analyst and a data scientist and exactly what they do. So let's not waste any time let's jump right onto my screen and take a look. All right so the first thing that we're going to take a look at is the responsibilities of a data analyst versus a data scientist. Starting with a data analyst some of the responsibilities are going to include cleaning and analyzing the static data, performing statistical and exploratory analysis, creating reports and visualizations, working directly with stakeholders to make data driven decisions and conduct ad hoc analysis. So let's break this down just a little bit further. So when I say clean and analyze static data that static data refers to data that is sitting in a database you're not working with live data or a live stream of data. So a client might be sending you an excel file every night which you ingest and put into your database and that's you know yesterday's data or you could be accessing their database and you could pull something every month using something like a stored procedure but most likely that data is just going to be sitting in a database somewhere and you're going to go and clean that data to make it more usable and then analyze that data as well to find trends and patterns or whatever the client needs. Now let's skip down to reports and visualizations. Now in my mind these are two separate things although at some companies they are the exact same thing. Now in my mind a report is something like an excel file or some type of analysis that I've done that I'm sending to a client and I can kind of automate that so maybe I'm sending that every week or every month and I send that to a client. A visualization to me is something like a Power BI or a Tableau dashboard or a hundred other tools that you could do even Python or R but it's some type of visualization of the data and sometimes you're going to send that to a client but sometimes you'll just have it online in some type of web application that a client can go and just check that visualization. The last one I want to touch on is this ad hoc analysis. This refers to just one off reports or one off questions that a client might have and this is something unfortunately that data analysts do a lot. You're going to be sitting at your computer doing your work all of a sudden you get an email from a client saying hey I need this analysis really quickly can you just grab that for me and of course it's not going to be something super simple it'll take you know a couple hours to do and so you go and you do that one off reports or that one off analysis and then you send that to the client. That unfortunately is just a big part of being a data analyst where you just get lots of requests for these kind of one off reports or one off types of analysis. Now let's take a look at data scientists. So a data scientist is going to use current data to discover future opportunities. They're going to use machine learning models and statistical methods to analyze the data. They're going to fine tune these models for improved accuracy. They're going to do a lot of data cleaning and modeling and conduct A, B testing. So this first one says they're going to use current data to discover future opportunities. What that means is they're going to use the current data to predict what's going to happen in the future at least to the best of their abilities. So thinking about something like Netflix you watch all these shows they collect that data. They have machine learning models running on the back end that basically say okay we have this data what shows would they want to watch next. And so they're using that current data to predict what you'll want in the future. These next ones relate mostly to machine learning models so using machine learning models and then fine tuning those models for accuracy. There are a lot of different methods in machine learning that a data scientist can use. Now typically most data scientists aren't going to actually write any of these models. They're using pre-existing models that already exist and they're plugging in the data into these models. A lot of times a data scientist will have some idea of what model is going to work best but they might test the data with one, two, three or even up to like five or ten models depending on what they're doing and then when they find the one that is the most accurate for their use case they're going to try to fine tune that. Now the next ones are data cleaning and modeling and then A, B testing. Data cleaning and data modeling is something that both data analysts and data scientists do but data analysts tend to be more on the data cleaning side and not as much of the modeling unless you're like a senior level or a lead or just depending on the role that you're in. Almost all data scientists are going to need to know how to model the data and really what that refers to is shaping and cleaning that data for whatever model you're putting the data into. Another last thing is A, B testing. Now A, B testing is basically where you're testing to see whether one option or another is better for what you're trying to do. The simplest way that I can explain it is you have a lemonade stand and in front of it you say 50 cents per cup and you test that out for a while and then you have another lemonade stand where you say half a dollar per cup. They're saying the same thing or doing something very similar you're just testing to see which one has the best results based off some metric that you've defined. Now I know that's super, super simplified it can be very complex but you're basically comparing two different things and seeing which one reacts better. So in general a data analyst is going to work with a client they're going to get data in we're going to help clean it then we're going to analyze that data. With that data we're going to create reports and visualizations and give it back to the client or the stakeholder or whoever it's for. Data scientists can also work with stakeholders and clients but not as much as a data analyst and they're going to use a lot more machine learning a lot more technical skills in order to kind of predict what's going to happen next. Of course they need to clean and model that data and make sure it's useful for their machine learning model and then they're going to get their results and they might even visualize that data but sometimes data scientists will work with data analysts to actually help them visualize the data. Now let's move on to technical skills and let's start off with the data analyst. So some of the technical skills that a data analyst is going to work with are things like SQL, R and Python and then within Python we have a few specific things that are just really popular things like pandas, pollers, numpy and matplotlib as well as a lot of others. We'll also use data visualization tools like Tableau and Power BI of course we'll be using Excel we may also use a cloud platform like AWS or Azure and then lastly some type of statistical tool like SAS or SPSS. Now these are just some of the more popular skills and tools that you're going to use as a data analyst but of course there are thousands out there. I've used a ton of other things that aren't on this list they're just not as popular for most data analysts. Now let's take a look at data scientists. They're going to use SQL, R and Python but the packages and the libraries may be a little bit different. They may use pandas, pollers, TensorFlow, scikit learn. They may also use a cloud platform like AWS or Azure, Excel, then they may use Spark, Docker and Kubernetes and then of course Git. You'll notice that with the technical skills there's quite a bit of crossover. They're both going to be using SQL, they're both going to be using Excel, some type of cloud platform, R and Python. There is a lot of crossover between these two but in general the data scientist is going to be a little bit more technical. Now let's take a look at education and let's start off with the data analyst. Now most jobs are going to require a bachelor's degree that's just pretty standard across all jobs. There are some jobs as well that do require a master's degree but typically it's just a bachelor's degree. Now you don't have to have a degree at all, it just makes it more difficult although I have worked with people who don't have a degree. If you are looking to become a data analyst here are some of the degrees that are really useful. Ones like statistics, mathematics, economics, computer science, business analytics and information systems. Now let's take a look at data scientists. Now typically you're going to need a bachelor's, a master's or sometimes even a PhD. Those PhD ones are far and few between but you will see more jobs requiring a master's for this type of position but almost all of them require at least a bachelor's degree. The education for a data scientist I think is a little bit more important. If you don't have a degree I think it's a little bit tougher to break into the data science space. Some of the degrees that you might want to have to become a data scientist are things like computer science, engineering, software engineering, information systems, information technology, statistics or physics. Now just to be transparent I have a degree in recreational therapy which is not on this list at all but I use that healthcare to kind of transition into data analytics. So you don't have to have one of these degrees in order to become a data analyst or a data scientist these are just some of the more recommended degrees. Now let's take a look at some of the job titles that you might see for a data analyst and a data scientist. Let's start off with the data analyst. The first of course is just going to be a typical data analyst title. That one's really common and you'll see that almost any job board that you check. You'll also see ones like quantitative analyst and technical analyst as well as ones that are domain specific. So things like healthcare, finance, marketing analyst. So just kind of insert your domain and then put analyst behind it. Typically there are jobs that are associated with those as well. Next for a data scientist we have of course a data scientist. This could be data scientist one, two, three. Same thing for data analyst you'll see data analyst one, two, three. We also have machine learning engineer and machine learning developer. So if you're looking to become a data analyst or data scientist these are some of the job titles that I would be looking for. Lastly let's take a look at the pay for both a data analyst and a data scientist. Let's start off with data analyst. For an entry level data analyst role you're looking at anywhere from 50 to 75. Now that's where you're going to get on average sometimes it'll be a little bit lower sometimes it might be a little bit higher. As a mid-level analyst you're looking at anywhere from 70 to 90,000 and as a senior level data analyst anywhere from 80 to 120,000. Now this is for the United States so if you're in another country you might want to take a look at what your averages are there. Now all these averages are very dependent on several factors for both the data analyst and the data scientist. It can definitely depend on your years of experience your education where you live all of these things play a factor into how much money you're going to make. But now let's take a look at the data scientist. For an entry-level role you'll be looking at 65 to 100,000 dollars. For a mid-level role anywhere from 85 to 120,000 and then for a senior data scientist role anywhere from 100 to 150,000. You'll notice some overlap on both of these where it's 65 to 100 versus 85 to 120. Those are just the averages I'm giving you the data as it is based off of you know these aggregations from Google, Glassdoor and LinkedIn data but they are pretty accurate. You're also going to notice that data scientists get paid more money I think this is because of the technical nature of the work. Data scientists tend to need a little bit more education it's a little bit more technical and difficult to understand some of the things that they're doing. So if you do have the expertise to be able to do that work they're going to pay you more money to do it. It is kind of a double-edged sword though because even though they make more money it's harder to land those jobs in my opinion whereas a data analyst they're a little bit easier to land a job. So that is the breakdown between a data analyst and a data scientist. Now if you're trying to become one of those there are many things that you should take into consideration. The first thing to consider is which one do you really like and have a passion for. If you really like analyzing data and finding those trends and patterns then data analysis is a great place for you but if you want to be a little bit more technical work with machine learning and maybe even AI kind of predict what's going to happen next then trying to become a data scientist might be great for you. Now just some of my unsolicited advice if you want to become a data scientist that's your end goal for you you can still start out as a data analyst especially if you don't have the education you don't have the technical skills just yet and you kind of want to work your way up to there a data analyst might be a great place to kind of leapfrog into a data scientist position. I've seen a ton of people do that some people just stay data analysts forever like me because that's really what I have a passion for but some people will use data analysis to kind of get some experience working with data and then they'll build up their technical skills and then become a data scientist. Again just some unsolicited advice they have a lot of kind of the core skills that they use then data scientists tend to just be a little bit more technical. So I hope that this was helpful and that you understand the difference between a data analyst and a data scientist now. If you did be sure to like and subscribe below and I will see you in the next video.